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bootComb—an R package to derive confidence intervals for combinations of independent parameter estimates
MOTIVATION: To address the limits of facility- or study-based estimates, multiple independent parameter estimates may need to be combined. Specific examples include (i) adjusting an incidence rate for healthcare utilisation, (ii) deriving a disease prevalence from a conditional prevalence and the pr...
Autor principal: | |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8407867/ http://dx.doi.org/10.1093/ije/dyab049 |
Sumario: | MOTIVATION: To address the limits of facility- or study-based estimates, multiple independent parameter estimates may need to be combined. Specific examples include (i) adjusting an incidence rate for healthcare utilisation, (ii) deriving a disease prevalence from a conditional prevalence and the prevalence of the underlying condition, (iii) adjusting a seroprevalence for test sensitivity and specificity. Calculating combined parameter estimates is generally straightforward, but deriving corresponding confidence intervals often is not. bootComb is an R package using parametric bootstrap sampling to derive such intervals. IMPLEMENTATION: bootComb is a package for the statistical computation environment R. GENERAL FEATURES: Apart from a function returning confidence intervals for parameters combined from several independent estimates, bootComb provides auxiliary functions for 6 common distributions (beta, normal, exponential, gamma, Poisson and negative binomial) to derive best-fit distributions for parameters given their reported confidence intervals. AVAILABILITY: bootComb is available from the Comprehensive R Archive Network (https://CRAN.R-project.org/package=bootComb). |
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